A gravitation-based link prediction approach in social networks

Bastami, Esmaeil; Mahabadi, Aminollah; Taghizadeh, Elias

doi:10.1016/j.swevo.2018.03.001

Cited by 39 publications

(22 citation statements)

References 17 publications

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“…Mahmoudi et al [89] outlined user community changes referred to as User Attribute-based Link Prediction (UALP). Bastami et al [87] proposed the gravitation-based link prediction approach with the integration of node features, community information, and graph properties.…”

Section: ) Community Similarity-based Methodsmentioning

confidence: 99%

“…New community-based methods are included in the similarity-based approach following the latest research trends on prediction links that utilize community information as predictive parameters [20], [87], [102], [113]. The similaritybased approaches include the local similarity-based, global similarity-based, quasi-local similarity-based, and community similarity-based methods, as shown in Fig.…”

Section: Proposed Link Prediction Taxonomymentioning

confidence: 99%

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Taxonomy of Link Prediction for Social Network Analysis: A Review

2020

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Link prediction is a technique to forecast future new or missing relationships between entities based on the current network information. Graph theory and network science are theoretical concepts that have influenced the link prediction research. Although previous reviews clearly outlined the link prediction research, it was focused on describing prediction approaches only. However, analysis of related studies identified other components that influence link prediction. This review aims to present a continued review and introduce the taxonomy of link prediction using three main components: the prediction approaches, prediction features, and prediction measurements. Each component has been detailed using its own taxonomy available at the present review. Furthermore, this review compares the prediction approaches and prediction features also benchmark algorithms and measurement methods of previous link prediction studies. In conclusion, the previous studies mostly focused on structural features and similarity-based approaches, while measuring the proposed methods using the Area Under the Curve (AUC) score. The proposed link prediction taxonomy can guide the researchers to generate new ideas and innovations that contribute to the link prediction research.

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Section: ) Community Similarity-based Methodsmentioning

confidence: 99%

Section: Proposed Link Prediction Taxonomymentioning

confidence: 99%

Taxonomy of Link Prediction for Social Network Analysis: A Review

2020

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“…In addition, the network structurebased similarity method allocates similarity scores to the node pairs according to the structure features of networks. Currently, the network structure-based similarity method mainly contains four categories, i.e., local approaches, global approaches, quasi-local approaches, and community-based approaches [10].…”

Section: Related Workmentioning

confidence: 99%

Link prediction in paper citation network to construct paper correlation graph

Liu

Kou

Yan

et al. 2019

J Wireless Com Network

119

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Nowadays, recommender system has become one of the main tools to search for users' interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user's need of keywords. Besides, to satisfy the users' requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors' information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance.

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“…Another important problem that needs to be addressed concerns the accuracy of link prediction approaches. [14] developed an innovative link-prediction method that aimed to improve the accuracy of link predictions. The experimental findings revealed that the proposed approach outperformed many other methods in terms of accuracy and scalability and the associated runtime was significantly less than that observed in previous studies.…”

Section: Predicting Links In Snmentioning

confidence: 99%

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Mohameds¹,

Zakis²,

Marjans³

2019

Adv. sci. technol. eng. syst. j.

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In more recent times, researchers have turned their attention to link prediction and the role link inference can play in better understanding the evolutionary nature of social networking sites. The objective of this paper is to present an in-depth review, analysis, and discussion of the cutting-edge link prediction methods that can be applied to better understand the development of social networks. The findings of the literature review reveal that there has been a steady increase in the number of published articles that present novel link prediction models that are designed to enhance the efficiency and accuracy of link prediction. In this paper, this most recent techniques to be proposed in this regard are compared and categorized, and features and evaluation metrics are presented for each approach. The results of the evaluation reveal that there are no complete or definitive methods available that can accurately and reliably be applied within different dynamic social networks to predict missing, emerging, and broken links within the network. The paper concludes by presenting potential future directions and recommendations for further studies

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A gravitation-based link prediction approach in social networks

Cited by 39 publications

References 17 publications

Taxonomy of Link Prediction for Social Network Analysis: A Review

Taxonomy of Link Prediction for Social Network Analysis: A Review

Link prediction in paper citation network to construct paper correlation graph

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

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